For decades, forest communities have relied on forests for their livelihood, culture, and ecological balance, which has led to traditional forest management. Although such systems are indigenous knowledge-based and sustainable, they are now being threatened by population increase, commercialization, and climate change. Forest ecosystems are under threat from overharvesting, deforestation, and loss of biodiversity; these issues are exacerbated by socioeconomic issues, outdated laws, and poor governance. The increasing impacts of pests, invasive species, and climate change highlight the shortcomings of conventional forest conservation practices. To this extent, machine learning (ML) and artificial intelligence (AI) offer new mechanisms for ensuring forests are sustainably managed. These technologies enable real-time satellite monitoring, predictive modeling for climate impact analysis, and data-driven decision-making for resource optimization. Moreover, by enabling early identification of diseases, forest fires, and illegal logging, AI-based technology enhances governance and conservation. A data-driven strategy for forest sustainability can be encouraged by adopting AI-based methods, which can help fill the gap between traditional methods and the current needs for conservation. To maximize the use of AI and ML towards safeguarding the forest ecosystem for future generations, governments, scientists, and local people need to cooperate.